{"product_id":"privacy-and-security-for-large-language-models-hands-on-privacy-preserving-techniques-for-personalized-ai-paperback","title":"Privacy and Security for Large Language Models: Hands-On Privacy-Preserving Techniques for Personalized AI - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eBaihan Lin\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eAs the deployment of AI technologies surges, the need to safeguard privacy and security in the use of large language models (LLMs) is more crucial than ever. Professionals face the challenge of leveraging the immense power of LLMs for personalized applications while ensuring stringent data privacy and security. The stakes are high, as privacy breaches and data leaks can lead to significant reputational and financial repercussions.\u003c\/p\u003e \u003cp\u003eThis book serves as a much-needed guide to addressing these pressing concerns. Dr. Baihan Lin offers a comprehensive exploration of privacy-preserving and security techniques like differential privacy, federated learning, and homomorphic encryption, applied specifically to LLMs. With its hands-on code examples, real-world case studies, and robust fine-tuning methodologies in domain-specific applications, this book is a vital resource for developing secure, ethical, and personalized AI solutions in today's privacy-conscious landscape.\u003c\/p\u003e \u003cp\u003eBy reading this book, you'll: \u003c\/p\u003e \u003cul\u003e \u003cli\u003eDiscover privacy-preserving techniques for LLMs\u003c\/li\u003e \u003cli\u003eLearn secure fine-tuning methodologies for personalizing LLMs\u003c\/li\u003e \u003cli\u003eUnderstand secure deployment strategies and protection against attacks\u003c\/li\u003e \u003cli\u003eExplore ethical considerations like bias and transparency\u003c\/li\u003e \u003cli\u003eGain insights from real-world case studies across healthcare, finance, and more\u003c\/li\u003e \u003c\/ul\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 315\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.67 x 9.19 x 7 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e February 17, 2026\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":44848424222822,"sku":"9781098160845","price":96.84,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0599\/7255\/0758\/files\/QMn9RS8fZq9781098160845.webp?v=1772751920","url":"https:\/\/infinitylightwa.com\/products\/privacy-and-security-for-large-language-models-hands-on-privacy-preserving-techniques-for-personalized-ai-paperback","provider":"Infinity Light","version":"1.0","type":"link"}